Overview

Dataset statistics

Number of variables20
Number of observations27976
Missing cells5959
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical10
Boolean1

Alerts

default is highly imbalanced (53.2%)Imbalance
loan is highly imbalanced (50.9%)Imbalance
poutcome is highly imbalanced (56.3%)Imbalance
age has 295 (1.1%) missing valuesMissing
job has 338 (1.2%) missing valuesMissing
marital has 298 (1.1%) missing valuesMissing
education has 304 (1.1%) missing valuesMissing
default has 285 (1.0%) missing valuesMissing
loan has 329 (1.2%) missing valuesMissing
contact has 293 (1.0%) missing valuesMissing
month has 287 (1.0%) missing valuesMissing
day_of_week has 294 (1.1%) missing valuesMissing
campaign has 298 (1.1%) missing valuesMissing
pdays has 324 (1.2%) missing valuesMissing
previous has 292 (1.0%) missing valuesMissing
poutcome has 312 (1.1%) missing valuesMissing
emp.var.rate has 300 (1.1%) missing valuesMissing
cons.conf.idx has 288 (1.0%) missing valuesMissing
euribor3m has 299 (1.1%) missing valuesMissing
nr.employed has 323 (1.2%) missing valuesMissing
previous has 23851 (85.3%) zerosZeros

Reproduction

Analysis started2024-03-03 18:54:28.890779
Analysis finished2024-03-03 18:54:55.024937
Duration26.13 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

age
Real number (ℝ)

MISSING 

Distinct77
Distinct (%)0.3%
Missing295
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean40.080597
Minimum17
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:54:55.215268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum95
Range78
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.403703
Coefficient of variation (CV)0.25956956
Kurtosis0.7279097
Mean40.080597
Median Absolute Deviation (MAD)7
Skewness0.77055109
Sum1109471
Variance108.23704
MonotonicityNot monotonic
2024-03-03T18:54:55.642204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1282
 
4.6%
32 1253
 
4.5%
33 1232
 
4.4%
36 1192
 
4.3%
35 1182
 
4.2%
34 1140
 
4.1%
30 1123
 
4.0%
37 1037
 
3.7%
39 962
 
3.4%
29 958
 
3.4%
Other values (67) 16320
58.3%
ValueCountFrequency (%)
17 3
 
< 0.1%
18 15
 
0.1%
19 26
 
0.1%
20 45
 
0.2%
21 61
 
0.2%
22 98
 
0.4%
23 146
 
0.5%
24 316
1.1%
25 395
1.4%
26 476
1.7%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 12
< 0.1%
87 1
 
< 0.1%
86 4
 
< 0.1%
85 6
< 0.1%
84 5
< 0.1%

job
Categorical

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing338
Missing (%)1.2%
Memory size437.1 KiB
admin.
6988 
blue-collar
6190 
technician
4510 
services
2673 
management
1969 
Other values (7)
5308 

Length

Max length13
Median length12
Mean length8.9522035
Min length6

Characters and Unicode

Total characters247421
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowadmin.
3rd rowblue-collar
4th rowadmin.
5th rowblue-collar

Common Values

ValueCountFrequency (%)
admin. 6988
25.0%
blue-collar 6190
22.1%
technician 4510
16.1%
services 2673
 
9.6%
management 1969
 
7.0%
retired 1179
 
4.2%
entrepreneur 967
 
3.5%
self-employed 948
 
3.4%
housemaid 722
 
2.6%
unemployed 702
 
2.5%
Other values (2) 790
 
2.8%

Length

2024-03-03T18:54:56.071284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 6988
25.3%
blue-collar 6190
22.4%
technician 4510
16.3%
services 2673
 
9.7%
management 1969
 
7.1%
retired 1179
 
4.3%
entrepreneur 967
 
3.5%
self-employed 948
 
3.4%
housemaid 722
 
2.6%
unemployed 702
 
2.5%
Other values (2) 790
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 31740
12.8%
n 23832
 
9.6%
a 22348
 
9.0%
l 21168
 
8.6%
i 20582
 
8.3%
c 17883
 
7.2%
r 14122
 
5.7%
m 13298
 
5.4%
d 11099
 
4.5%
t 9745
 
3.9%
Other values (14) 61604
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 233295
94.3%
Dash Punctuation 7138
 
2.9%
Other Punctuation 6988
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31740
13.6%
n 23832
10.2%
a 22348
9.6%
l 21168
9.1%
i 20582
8.8%
c 17883
 
7.7%
r 14122
 
6.1%
m 13298
 
5.7%
d 11099
 
4.8%
t 9745
 
4.2%
Other values (12) 47478
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 7138
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233295
94.3%
Common 14126
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31740
13.6%
n 23832
10.2%
a 22348
9.6%
l 21168
9.1%
i 20582
8.8%
c 17883
 
7.7%
r 14122
 
6.1%
m 13298
 
5.7%
d 11099
 
4.8%
t 9745
 
4.2%
Other values (12) 47478
20.4%
Common
ValueCountFrequency (%)
- 7138
50.5%
. 6988
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31740
12.8%
n 23832
 
9.6%
a 22348
 
9.0%
l 21168
 
8.6%
i 20582
 
8.3%
c 17883
 
7.2%
r 14122
 
5.7%
m 13298
 
5.4%
d 11099
 
4.5%
t 9745
 
3.9%
Other values (14) 61604
24.9%

marital
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing298
Missing (%)1.1%
Memory size437.1 KiB
married
16794 
single
7702 
divorced
3123 
unknown
 
59

Length

Max length8
Median length7
Mean length6.8345617
Min length6

Characters and Unicode

Total characters189167
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdivorced
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 16794
60.0%
single 7702
27.5%
divorced 3123
 
11.2%
unknown 59
 
0.2%
(Missing) 298
 
1.1%

Length

2024-03-03T18:54:56.484418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:54:56.806960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
married 16794
60.7%
single 7702
27.8%
divorced 3123
 
11.3%
unknown 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 36711
19.4%
i 27619
14.6%
e 27619
14.6%
d 23040
12.2%
m 16794
8.9%
a 16794
8.9%
n 7879
 
4.2%
s 7702
 
4.1%
g 7702
 
4.1%
l 7702
 
4.1%
Other values (6) 9605
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 189167
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 36711
19.4%
i 27619
14.6%
e 27619
14.6%
d 23040
12.2%
m 16794
8.9%
a 16794
8.9%
n 7879
 
4.2%
s 7702
 
4.1%
g 7702
 
4.1%
l 7702
 
4.1%
Other values (6) 9605
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 189167
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 36711
19.4%
i 27619
14.6%
e 27619
14.6%
d 23040
12.2%
m 16794
8.9%
a 16794
8.9%
n 7879
 
4.2%
s 7702
 
4.1%
g 7702
 
4.1%
l 7702
 
4.1%
Other values (6) 9605
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 36711
19.4%
i 27619
14.6%
e 27619
14.6%
d 23040
12.2%
m 16794
8.9%
a 16794
8.9%
n 7879
 
4.2%
s 7702
 
4.1%
g 7702
 
4.1%
l 7702
 
4.1%
Other values (6) 9605
 
5.1%

education
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing304
Missing (%)1.1%
Memory size437.1 KiB
university.degree
8157 
high.school
6375 
basic.9y
4087 
professional.course
3501 
basic.4y
2818 
Other values (3)
2734 

Length

Max length19
Median length17
Mean length12.695071
Min length7

Characters and Unicode

Total characters351298
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuniversity.degree
2nd rowuniversity.degree
3rd rowhigh.school
4th rowhigh.school
5th rowbasic.4y

Common Values

ValueCountFrequency (%)
university.degree 8157
29.2%
high.school 6375
22.8%
basic.9y 4087
14.6%
professional.course 3501
12.5%
basic.4y 2818
 
10.1%
basic.6y 1571
 
5.6%
unknown 1151
 
4.1%
illiterate 12
 
< 0.1%
(Missing) 304
 
1.1%

Length

2024-03-03T18:54:57.166810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:54:57.541712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 8157
29.5%
high.school 6375
23.0%
basic.9y 4087
14.8%
professional.course 3501
12.7%
basic.4y 2818
 
10.2%
basic.6y 1571
 
5.7%
unknown 1151
 
4.2%
illiterate 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 39654
 
11.3%
i 34690
 
9.9%
s 33511
 
9.5%
. 26509
 
7.5%
o 24404
 
6.9%
r 23328
 
6.6%
h 19125
 
5.4%
c 18352
 
5.2%
y 16633
 
4.7%
n 15111
 
4.3%
Other values (15) 99981
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316313
90.0%
Other Punctuation 26509
 
7.5%
Decimal Number 8476
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39654
12.5%
i 34690
11.0%
s 33511
10.6%
o 24404
 
7.7%
r 23328
 
7.4%
h 19125
 
6.0%
c 18352
 
5.8%
y 16633
 
5.3%
n 15111
 
4.8%
g 14532
 
4.6%
Other values (11) 76973
24.3%
Decimal Number
ValueCountFrequency (%)
9 4087
48.2%
4 2818
33.2%
6 1571
 
18.5%
Other Punctuation
ValueCountFrequency (%)
. 26509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316313
90.0%
Common 34985
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39654
12.5%
i 34690
11.0%
s 33511
10.6%
o 24404
 
7.7%
r 23328
 
7.4%
h 19125
 
6.0%
c 18352
 
5.8%
y 16633
 
5.3%
n 15111
 
4.8%
g 14532
 
4.6%
Other values (11) 76973
24.3%
Common
ValueCountFrequency (%)
. 26509
75.8%
9 4087
 
11.7%
4 2818
 
8.1%
6 1571
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39654
 
11.3%
i 34690
 
9.9%
s 33511
 
9.5%
. 26509
 
7.5%
o 24404
 
6.9%
r 23328
 
6.6%
h 19125
 
5.4%
c 18352
 
5.2%
y 16633
 
4.7%
n 15111
 
4.3%
Other values (15) 99981
28.5%

default
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing285
Missing (%)1.0%
Memory size437.1 KiB
no
21882 
unknown
5806 
yes
 
3

Length

Max length7
Median length2
Mean length3.0484634
Min length2

Characters and Unicode

Total characters84415
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowunknown

Common Values

ValueCountFrequency (%)
no 21882
78.2%
unknown 5806
 
20.8%
yes 3
 
< 0.1%
(Missing) 285
 
1.0%

Length

2024-03-03T18:54:58.042075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:54:58.365994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 21882
79.0%
unknown 5806
 
21.0%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 39300
46.6%
o 27688
32.8%
u 5806
 
6.9%
k 5806
 
6.9%
w 5806
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84415
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 39300
46.6%
o 27688
32.8%
u 5806
 
6.9%
k 5806
 
6.9%
w 5806
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 84415
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 39300
46.6%
o 27688
32.8%
u 5806
 
6.9%
k 5806
 
6.9%
w 5806
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 39300
46.6%
o 27688
32.8%
u 5806
 
6.9%
k 5806
 
6.9%
w 5806
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing267
Missing (%)1.0%
Memory size437.1 KiB
yes
14492 
no
12560 
unknown
 
657

Length

Max length7
Median length3
Mean length2.6415605
Min length2

Characters and Unicode

Total characters73195
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowyes
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes 14492
51.8%
no 12560
44.9%
unknown 657
 
2.3%
(Missing) 267
 
1.0%

Length

2024-03-03T18:54:58.688243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:54:58.953248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 14492
52.3%
no 12560
45.3%
unknown 657
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 14531
19.9%
y 14492
19.8%
e 14492
19.8%
s 14492
19.8%
o 13217
18.1%
u 657
 
0.9%
k 657
 
0.9%
w 657
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73195
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 14531
19.9%
y 14492
19.8%
e 14492
19.8%
s 14492
19.8%
o 13217
18.1%
u 657
 
0.9%
k 657
 
0.9%
w 657
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 73195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 14531
19.9%
y 14492
19.8%
e 14492
19.8%
s 14492
19.8%
o 13217
18.1%
u 657
 
0.9%
k 657
 
0.9%
w 657
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 14531
19.9%
y 14492
19.8%
e 14492
19.8%
s 14492
19.8%
o 13217
18.1%
u 657
 
0.9%
k 657
 
0.9%
w 657
 
0.9%

loan
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing329
Missing (%)1.2%
Memory size437.1 KiB
no
22706 
yes
4285 
unknown
 
656

Length

Max length7
Median length2
Mean length2.2736282
Min length2

Characters and Unicode

Total characters62859
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 22706
81.2%
yes 4285
 
15.3%
unknown 656
 
2.3%
(Missing) 329
 
1.2%

Length

2024-03-03T18:54:59.268337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:54:59.549526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 22706
82.1%
yes 4285
 
15.5%
unknown 656
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 24674
39.3%
o 23362
37.2%
y 4285
 
6.8%
e 4285
 
6.8%
s 4285
 
6.8%
u 656
 
1.0%
k 656
 
1.0%
w 656
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62859
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 24674
39.3%
o 23362
37.2%
y 4285
 
6.8%
e 4285
 
6.8%
s 4285
 
6.8%
u 656
 
1.0%
k 656
 
1.0%
w 656
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62859
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 24674
39.3%
o 23362
37.2%
y 4285
 
6.8%
e 4285
 
6.8%
s 4285
 
6.8%
u 656
 
1.0%
k 656
 
1.0%
w 656
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 24674
39.3%
o 23362
37.2%
y 4285
 
6.8%
e 4285
 
6.8%
s 4285
 
6.8%
u 656
 
1.0%
k 656
 
1.0%
w 656
 
1.0%

contact
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing293
Missing (%)1.0%
Memory size437.1 KiB
cellular
17408 
telephone
10275 

Length

Max length9
Median length8
Mean length8.3711664
Min length8

Characters and Unicode

Total characters231739
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowcellular
3rd rowcellular
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 17408
62.2%
telephone 10275
36.7%
(Missing) 293
 
1.0%

Length

2024-03-03T18:54:59.899459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:55:00.231668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 17408
62.9%
telephone 10275
37.1%

Most occurring characters

ValueCountFrequency (%)
l 62499
27.0%
e 48233
20.8%
c 17408
 
7.5%
u 17408
 
7.5%
a 17408
 
7.5%
r 17408
 
7.5%
t 10275
 
4.4%
p 10275
 
4.4%
h 10275
 
4.4%
o 10275
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231739
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 62499
27.0%
e 48233
20.8%
c 17408
 
7.5%
u 17408
 
7.5%
a 17408
 
7.5%
r 17408
 
7.5%
t 10275
 
4.4%
p 10275
 
4.4%
h 10275
 
4.4%
o 10275
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 231739
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 62499
27.0%
e 48233
20.8%
c 17408
 
7.5%
u 17408
 
7.5%
a 17408
 
7.5%
r 17408
 
7.5%
t 10275
 
4.4%
p 10275
 
4.4%
h 10275
 
4.4%
o 10275
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 62499
27.0%
e 48233
20.8%
c 17408
 
7.5%
u 17408
 
7.5%
a 17408
 
7.5%
r 17408
 
7.5%
t 10275
 
4.4%
p 10275
 
4.4%
h 10275
 
4.4%
o 10275
 
4.4%

month
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing287
Missing (%)1.0%
Memory size437.1 KiB
may
9245 
jul
4774 
aug
4050 
jun
3651 
nov
2773 
Other values (5)
3196 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters83067
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownov
2nd rowaug
3rd rowapr
4th rowjun
5th rowjun

Common Values

ValueCountFrequency (%)
may 9245
33.0%
jul 4774
17.1%
aug 4050
14.5%
jun 3651
 
13.1%
nov 2773
 
9.9%
apr 1826
 
6.5%
oct 504
 
1.8%
sep 379
 
1.4%
mar 363
 
1.3%
dec 124
 
0.4%
(Missing) 287
 
1.0%

Length

2024-03-03T18:55:00.522298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:55:00.835993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
may 9245
33.4%
jul 4774
17.2%
aug 4050
14.6%
jun 3651
 
13.2%
nov 2773
 
10.0%
apr 1826
 
6.6%
oct 504
 
1.8%
sep 379
 
1.4%
mar 363
 
1.3%
dec 124
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 15484
18.6%
u 12475
15.0%
m 9608
11.6%
y 9245
11.1%
j 8425
10.1%
n 6424
7.7%
l 4774
 
5.7%
g 4050
 
4.9%
o 3277
 
3.9%
v 2773
 
3.3%
Other values (7) 6532
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83067
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15484
18.6%
u 12475
15.0%
m 9608
11.6%
y 9245
11.1%
j 8425
10.1%
n 6424
7.7%
l 4774
 
5.7%
g 4050
 
4.9%
o 3277
 
3.9%
v 2773
 
3.3%
Other values (7) 6532
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 83067
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15484
18.6%
u 12475
15.0%
m 9608
11.6%
y 9245
11.1%
j 8425
10.1%
n 6424
7.7%
l 4774
 
5.7%
g 4050
 
4.9%
o 3277
 
3.9%
v 2773
 
3.3%
Other values (7) 6532
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15484
18.6%
u 12475
15.0%
m 9608
11.6%
y 9245
11.1%
j 8425
10.1%
n 6424
7.7%
l 4774
 
5.7%
g 4050
 
4.9%
o 3277
 
3.9%
v 2773
 
3.3%
Other values (7) 6532
7.9%

day_of_week
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing294
Missing (%)1.1%
Memory size437.1 KiB
mon
5790 
thu
5756 
tue
5416 
wed
5403 
fri
5317 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters83046
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowthu
5th rowwed

Common Values

ValueCountFrequency (%)
mon 5790
20.7%
thu 5756
20.6%
tue 5416
19.4%
wed 5403
19.3%
fri 5317
19.0%
(Missing) 294
 
1.1%

Length

2024-03-03T18:55:01.242618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:55:01.540795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mon 5790
20.9%
thu 5756
20.8%
tue 5416
19.6%
wed 5403
19.5%
fri 5317
19.2%

Most occurring characters

ValueCountFrequency (%)
t 11172
13.5%
u 11172
13.5%
e 10819
13.0%
m 5790
7.0%
n 5790
7.0%
o 5790
7.0%
h 5756
6.9%
w 5403
6.5%
d 5403
6.5%
f 5317
6.4%
Other values (2) 10634
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83046
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 11172
13.5%
u 11172
13.5%
e 10819
13.0%
m 5790
7.0%
n 5790
7.0%
o 5790
7.0%
h 5756
6.9%
w 5403
6.5%
d 5403
6.5%
f 5317
6.4%
Other values (2) 10634
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 83046
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 11172
13.5%
u 11172
13.5%
e 10819
13.0%
m 5790
7.0%
n 5790
7.0%
o 5790
7.0%
h 5756
6.9%
w 5403
6.5%
d 5403
6.5%
f 5317
6.4%
Other values (2) 10634
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 11172
13.5%
u 11172
13.5%
e 10819
13.0%
m 5790
7.0%
n 5790
7.0%
o 5790
7.0%
h 5756
6.9%
w 5403
6.5%
d 5403
6.5%
f 5317
6.4%
Other values (2) 10634
12.8%

campaign
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)0.1%
Missing298
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.6137365
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:02.015267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8482565
Coefficient of variation (CV)1.0897259
Kurtosis37.010428
Mean2.6137365
Median Absolute Deviation (MAD)1
Skewness4.7774684
Sum72343
Variance8.1125649
MonotonicityNot monotonic
2024-03-03T18:55:02.384356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 11586
41.4%
2 7196
25.7%
3 3647
 
13.0%
4 1792
 
6.4%
5 1110
 
4.0%
6 685
 
2.4%
7 408
 
1.5%
8 272
 
1.0%
9 201
 
0.7%
10 155
 
0.6%
Other values (30) 626
 
2.2%
(Missing) 298
 
1.1%
ValueCountFrequency (%)
1 11586
41.4%
2 7196
25.7%
3 3647
 
13.0%
4 1792
 
6.4%
5 1110
 
4.0%
6 685
 
2.4%
7 408
 
1.5%
8 272
 
1.0%
9 201
 
0.7%
10 155
 
0.6%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%
32 3
< 0.1%
31 5
< 0.1%

pdays
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)0.1%
Missing324
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean961.51136
Minimum0
Maximum999
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:02.791974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation189.26346
Coefficient of variation (CV)0.19683954
Kurtosis21.531509
Mean961.51136
Median Absolute Deviation (MAD)0
Skewness-4.8507254
Sum26587712
Variance35820.656
MonotonicityNot monotonic
2024-03-03T18:55:03.157317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
999 26608
95.1%
6 294
 
1.1%
3 293
 
1.0%
4 84
 
0.3%
9 47
 
0.2%
7 42
 
0.2%
10 38
 
0.1%
2 37
 
0.1%
12 35
 
0.1%
5 34
 
0.1%
Other values (15) 140
 
0.5%
(Missing) 324
 
1.2%
ValueCountFrequency (%)
0 10
 
< 0.1%
1 17
 
0.1%
2 37
 
0.1%
3 293
1.0%
4 84
 
0.3%
5 34
 
0.1%
6 294
1.1%
7 42
 
0.2%
8 13
 
< 0.1%
9 47
 
0.2%
ValueCountFrequency (%)
999 26608
95.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
19 2
 
< 0.1%
18 6
 
< 0.1%
17 6
 
< 0.1%
16 6
 
< 0.1%
15 14
 
0.1%

previous
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing292
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.17526369
Minimum0
Maximum7
Zeros23851
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:03.499923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49851206
Coefficient of variation (CV)2.8443545
Kurtosis20.150652
Mean0.17526369
Median Absolute Deviation (MAD)0
Skewness3.8315739
Sum4852
Variance0.24851427
MonotonicityNot monotonic
2024-03-03T18:55:03.839434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23851
85.3%
1 3117
 
11.1%
2 503
 
1.8%
3 145
 
0.5%
4 50
 
0.2%
5 15
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 292
 
1.0%
ValueCountFrequency (%)
0 23851
85.3%
1 3117
 
11.1%
2 503
 
1.8%
3 145
 
0.5%
4 50
 
0.2%
5 15
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 15
 
0.1%
4 50
 
0.2%
3 145
 
0.5%
2 503
 
1.8%
1 3117
 
11.1%
0 23851
85.3%

poutcome
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing312
Missing (%)1.1%
Memory size437.1 KiB
nonexistent
23832 
failure
2887 
success
 
945

Length

Max length11
Median length11
Mean length10.445922
Min length7

Characters and Unicode

Total characters288976
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 23832
85.2%
failure 2887
 
10.3%
success 945
 
3.4%
(Missing) 312
 
1.1%

Length

2024-03-03T18:55:04.248821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T18:55:04.580983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 23832
86.1%
failure 2887
 
10.4%
success 945
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 71496
24.7%
e 51496
17.8%
t 47664
16.5%
i 26719
 
9.2%
s 26667
 
9.2%
x 23832
 
8.2%
o 23832
 
8.2%
u 3832
 
1.3%
f 2887
 
1.0%
a 2887
 
1.0%
Other values (3) 7664
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 288976
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 71496
24.7%
e 51496
17.8%
t 47664
16.5%
i 26719
 
9.2%
s 26667
 
9.2%
x 23832
 
8.2%
o 23832
 
8.2%
u 3832
 
1.3%
f 2887
 
1.0%
a 2887
 
1.0%
Other values (3) 7664
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 288976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 71496
24.7%
e 51496
17.8%
t 47664
16.5%
i 26719
 
9.2%
s 26667
 
9.2%
x 23832
 
8.2%
o 23832
 
8.2%
u 3832
 
1.3%
f 2887
 
1.0%
a 2887
 
1.0%
Other values (3) 7664
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 71496
24.7%
e 51496
17.8%
t 47664
16.5%
i 26719
 
9.2%
s 26667
 
9.2%
x 23832
 
8.2%
o 23832
 
8.2%
u 3832
 
1.3%
f 2887
 
1.0%
a 2887
 
1.0%
Other values (3) 7664
 
2.7%

emp.var.rate
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing300
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean0.073399335
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative11630
Negative (%)41.6%
Memory size437.1 KiB
2024-03-03T18:55:04.845660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5727873
Coefficient of variation (CV)21.427814
Kurtosis-1.0760517
Mean0.073399335
Median Absolute Deviation (MAD)0.3
Skewness-0.71407215
Sum2031.4
Variance2.4736599
MonotonicityNot monotonic
2024-03-03T18:55:05.157066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 10831
38.7%
-1.8 6207
22.2%
1.1 5215
18.6%
-0.1 2491
 
8.9%
-2.9 1122
 
4.0%
-3.4 729
 
2.6%
-1.7 525
 
1.9%
-1.1 435
 
1.6%
-3 114
 
0.4%
-0.2 7
 
< 0.1%
(Missing) 300
 
1.1%
ValueCountFrequency (%)
-3.4 729
 
2.6%
-3 114
 
0.4%
-2.9 1122
 
4.0%
-1.8 6207
22.2%
-1.7 525
 
1.9%
-1.1 435
 
1.6%
-0.2 7
 
< 0.1%
-0.1 2491
 
8.9%
1.1 5215
18.6%
1.4 10831
38.7%
ValueCountFrequency (%)
1.4 10831
38.7%
1.1 5215
18.6%
-0.1 2491
 
8.9%
-0.2 7
 
< 0.1%
-1.1 435
 
1.6%
-1.7 525
 
1.9%
-1.8 6207
22.2%
-2.9 1122
 
4.0%
-3 114
 
0.4%
-3.4 729
 
2.6%

cons.price.idx
Real number (ℝ)

Distinct26
Distinct (%)0.1%
Missing263
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean93.577364
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:06.055887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.58185048
Coefficient of variation (CV)0.0062178549
Kurtosis-0.84193371
Mean93.577364
Median Absolute Deviation (MAD)0.45
Skewness-0.22940739
Sum2593309.5
Variance0.33854998
MonotonicityNot monotonic
2024-03-03T18:55:06.426858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 5223
18.7%
93.918 4446
15.9%
92.893 3898
13.9%
93.444 3372
12.1%
94.465 3027
10.8%
93.2 2436
8.7%
93.075 1701
 
6.1%
92.201 531
 
1.9%
92.963 486
 
1.7%
92.431 309
 
1.1%
Other values (16) 2284
8.2%
(Missing) 263
 
0.9%
ValueCountFrequency (%)
92.201 531
 
1.9%
92.379 172
 
0.6%
92.431 309
 
1.1%
92.469 109
 
0.4%
92.649 247
 
0.9%
92.713 117
 
0.4%
92.756 7
 
< 0.1%
92.843 183
 
0.7%
92.893 3898
13.9%
92.963 486
 
1.7%
ValueCountFrequency (%)
94.767 84
 
0.3%
94.601 145
 
0.5%
94.465 3027
10.8%
94.215 224
 
0.8%
94.199 208
 
0.7%
94.055 143
 
0.5%
94.027 156
 
0.6%
93.994 5223
18.7%
93.918 4446
15.9%
93.876 140
 
0.5%

cons.conf.idx
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)0.1%
Missing288
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-40.524187
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative27688
Negative (%)99.0%
Memory size437.1 KiB
2024-03-03T18:55:06.754997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6323267
Coefficient of variation (CV)-0.11431017
Kurtosis-0.3448797
Mean-40.524187
Median Absolute Deviation (MAD)4.4
Skewness0.31583362
Sum-1122033.7
Variance21.458451
MonotonicityNot monotonic
2024-03-03T18:55:07.090317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 5228
18.7%
-42.7 4449
15.9%
-46.2 3888
13.9%
-36.1 3365
12.0%
-41.8 3017
10.8%
-42 2447
8.7%
-47.1 1698
 
6.1%
-31.4 531
 
1.9%
-40.8 481
 
1.7%
-26.9 305
 
1.1%
Other values (16) 2279
8.1%
(Missing) 288
 
1.0%
ValueCountFrequency (%)
-50.8 85
 
0.3%
-50 182
 
0.7%
-49.5 145
 
0.5%
-47.1 1698
 
6.1%
-46.2 3888
13.9%
-45.9 7
 
< 0.1%
-42.7 4449
15.9%
-42 2447
8.7%
-41.8 3017
10.8%
-40.8 481
 
1.7%
ValueCountFrequency (%)
-26.9 305
 
1.1%
-29.8 169
 
0.6%
-30.1 249
 
0.9%
-31.4 531
 
1.9%
-33 116
 
0.4%
-33.6 109
 
0.4%
-34.6 125
 
0.4%
-34.8 179
 
0.6%
-36.1 3365
12.0%
-36.4 5228
18.7%

euribor3m
Real number (ℝ)

MISSING 

Distinct309
Distinct (%)1.1%
Missing299
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean3.6097348
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:07.419297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.7896
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7383369
Coefficient of variation (CV)0.48156913
Kurtosis-1.4260586
Mean3.6097348
Median Absolute Deviation (MAD)0.108
Skewness-0.69569656
Sum99906.631
Variance3.0218151
MonotonicityNot monotonic
2024-03-03T18:55:07.774170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 1921
 
6.9%
4.962 1727
 
6.2%
4.963 1667
 
6.0%
4.961 1299
 
4.6%
1.405 815
 
2.9%
4.856 794
 
2.8%
4.964 768
 
2.7%
4.965 715
 
2.6%
4.96 696
 
2.5%
4.864 693
 
2.5%
Other values (299) 16582
59.3%
ValueCountFrequency (%)
0.634 4
 
< 0.1%
0.635 31
0.1%
0.636 11
 
< 0.1%
0.637 5
 
< 0.1%
0.638 5
 
< 0.1%
0.639 11
 
< 0.1%
0.64 7
 
< 0.1%
0.642 25
0.1%
0.643 17
0.1%
0.644 26
0.1%
ValueCountFrequency (%)
5.045 4
 
< 0.1%
5 6
 
< 0.1%
4.97 101
 
0.4%
4.968 640
 
2.3%
4.967 408
 
1.5%
4.966 402
 
1.4%
4.965 715
2.6%
4.964 768
2.7%
4.963 1667
6.0%
4.962 1727
6.2%

nr.employed
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing323
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean5166.6135
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size437.1 KiB
2024-03-03T18:55:08.080919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.42301
Coefficient of variation (CV)0.014017501
Kurtosis-0.020532856
Mean5166.6135
Median Absolute Deviation (MAD)37.1
Skewness-1.036466
Sum1.4287236 × 108
Variance5245.0924
MonotonicityNot monotonic
2024-03-03T18:55:08.388182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 10815
38.7%
5099.1 5765
20.6%
5191 5226
18.7%
5195.8 2480
 
8.9%
5076.2 1117
 
4.0%
5017.5 729
 
2.6%
4991.6 518
 
1.9%
5008.7 441
 
1.6%
4963.6 438
 
1.6%
5023.5 117
 
0.4%
(Missing) 323
 
1.2%
ValueCountFrequency (%)
4963.6 438
 
1.6%
4991.6 518
 
1.9%
5008.7 441
 
1.6%
5017.5 729
 
2.6%
5023.5 117
 
0.4%
5076.2 1117
 
4.0%
5099.1 5765
20.6%
5176.3 7
 
< 0.1%
5191 5226
18.7%
5195.8 2480
8.9%
ValueCountFrequency (%)
5228.1 10815
38.7%
5195.8 2480
 
8.9%
5191 5226
18.7%
5176.3 7
 
< 0.1%
5099.1 5765
20.6%
5076.2 1117
 
4.0%
5023.5 117
 
0.4%
5017.5 729
 
2.6%
5008.7 441
 
1.6%
4991.6 518
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing270
Missing (%)1.0%
Memory size273.2 KiB
False
24500 
True
3206 
(Missing)
 
270
ValueCountFrequency (%)
False 24500
87.6%
True 3206
 
11.5%
(Missing) 270
 
1.0%
2024-03-03T18:55:08.690356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-03-03T18:54:51.165889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:32.024407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:34.445119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:36.804892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:39.435561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:41.898241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:44.054257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:46.361909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:48.604267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:51.411264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:32.361937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:34.704240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:37.080963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:39.720419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:42.170315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:44.311607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:46.610405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:48.864335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:51.683790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:32.619866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:35.023521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:37.339900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:40.021069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:42.406803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:44.585202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:46.866215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:49.126438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:51.937086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:32.868942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:35.319156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:37.595721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:40.308889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:42.653839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:44.843005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:47.103218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:49.372676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:52.208353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:33.145654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:35.566843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:38.148238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:40.596558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:42.895324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:45.135853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:47.359807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:49.945696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:52.450105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:33.428456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:35.809030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:38.401404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:40.834371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:43.108985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:45.383568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:47.607805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:50.181007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:52.675837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:33.689129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:36.049416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:38.661038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:41.083741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:43.329788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:45.623641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:47.857886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:50.427155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:52.903343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:33.931664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:36.281420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:38.911102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:41.337209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:43.574730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:45.868553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:48.101940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:50.685475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:53.152486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:34.190168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:36.542385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:39.179171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:41.630094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:43.806829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:46.110913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:48.355042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-03T18:54:50.925282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-03T18:54:53.554039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T18:54:54.317115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
2337055.0admin.divorceduniversity.degreenoyesnocellularnovmon2.0999.00.0nonexistent-0.193.200-42.04.1915195.8no
1840839.0admin.marrieduniversity.degreenononocellularaugmon2.0999.00.0nonexistent1.493.444-36.14.9705228.1no
2850337.0blue-collarmarriedhigh.schoolnoyesnocellularaprmon5.0999.00.0nonexistent-1.893.075-47.11.4055099.1no
1098130.0admin.marriedhigh.schoolnononotelephonejunthu1.0999.00.0nonexistent1.494.465-41.84.9615228.1no
1080943.0blue-collarmarriedbasic.4yunknownyesnotelephonejunwed2.0999.00.0nonexistent1.494.465-41.84.9625228.1no
1319657.0retiredmarriedbasic.4ynoyesnocellularjulthu1.0999.00.0nonexistent1.493.918-42.74.9635228.1no
34253NaNtechniciansingleuniversity.degreenononocellularmaymon4.0999.00.0nonexistent-1.892.893-46.21.2445099.1no
1411538.0blue-collarsinglebasic.4yunknownnonotelephonejultue5.0999.00.0nonexistent1.493.918-42.74.9615228.1no
742946.0blue-collarmarriedbasic.9yunknownnonotelephonemayfri1.0999.00.0nonexistent1.193.994-36.44.8645191.0no
2375332.0blue-collarmarriedbasic.9ynononocellularnovmon2.05.01.0success-0.193.200-42.04.1915195.8yes
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
168532.0servicesmarriedhigh.schoolnononotelephonemayfri2.0999.00.0nonexistent1.193.994-36.44.8555191.0no
1602326.0admin.singleuniversity.degreenoyesnocellularjulwed1.0999.00.0nonexistent1.493.918-42.74.9635228.1no
2196230.0techniciandivorceduniversity.degreenononocellularaugmon5.0999.00.0nonexistent1.493.444-36.14.9655228.1no
3719439.0admin.singlehigh.schoolnoyesnocellularocttue1.0999.00.0nonexistent-3.492.431-26.90.7375017.5yes
1685059.0admin.divorcedhigh.schoolnoyesyescellularjulmon2.0999.00.0nonexistent1.493.918-42.74.9625228.1no
626545.0admin.singleprofessional.coursenononotelephonemaytue4.0999.00.0nonexistent1.193.994-36.44.8575191.0no
1128450.0servicesdivorcedprofessional.coursenoyesnotelephonejunfri1.0999.00.0nonexistent1.494.465-41.84.9595228.1no
3815858.0servicesmarriedhigh.schoolnoyesnotelephonemarmon4.0999.00.0nonexistent-1.893.369-34.80.6355008.7no
86044.0blue-collarmarriedbasic.6ynoyesyestelephonemaywed1.0999.00.0nonexistent1.193.994-36.44.8565191.0no
1579549.0admin.marriedbasic.9ynoyesyescellularjultue4.0999.00.0nonexistent1.493.918-42.74.9615228.1no